Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. This book provides practical modeling methods for many real-world problems with high dimensionality or complexity which have not hitherto been treatable with Markov decision processes. In addition to providing numerous specific algorithms, coverage includes both illustrative numerical examples and rigorous theoretical convergence results. The algorithms developed and analyzed differ from the successful computational methods for solving MDPs based on neuro-dynamic programming or reinforcement learning and will complement work in those areas. In addition, the book shows how to combine the various algorithms introduced with approximate dynamic programming methods that reduce the size of the state space and ameliorate the effects of dimensionality.

Provides practical modeling methods for many real-world problems with high dimensionality or complextity which have not hitherto been treatable with Markov decision processes Rigorous theoretical derivation of sampling and population-based algorithms enables the reader to expand on the work presented in the certainty that new results will have a sound foundation First-time assimilation of many recently-developed techniques and results in a form suitable for a broad readership of researchers and students Includes supplementary material: sn.pub/extras

Autorentext

Hyeong Soo Chang (SM'07 of the IEEE, Member of INFORMS) received the B.S. and M.S. degrees in electrical engineering and the Ph.D. degree in electrical and computer engineering, all from Purdue University,West Lafayette, IN, in 1994, 1996, and 2001, respectively. Since 2003, he has been with the Department of Computer Science and Engineering, Sogang University, Seoul, Korea, where he is now an Associate Professor. He has about 30 journal papers in the area of MDPs and related areas. His main research interests include Markov decision processes, Markov games, computational learning theory, computational intelligence, and stochastic optimization. He currently serves as an Associate Editor for the IEEE Transactions on Automatic Control. Jiaqiao Hu (M'11 of the IEEE, Member of INFORMS) received the B.S. degree in automation from Shanghai Jiao Tong University, Shanghai, China, in 1997, the M.S. degree in applied mathematics from the University of Maryland, Baltimore County, in 2001, and the Ph.D. degree in electrical engineering from the University of Maryland, College Park, in 2006. Since 2006, he has been with the Department of Applied Mathematics and Statistics, State University of New York, Stony Brook, where he is currently an Assistant Professor Markov decision processes, simulation-based optimization, global optimization, applied probability, and stochastic modeling and analysis. Michael Fu (Fellow of the IEEE, Member of INFORMS) received his Ph.D. and M.S degrees in applied mathematics from Harvard University in 1989 and 1986, respectively. He received S.B. and S.M. degrees in electrical engineering and an S.B. degree in mathematics from the Massachusetts Institute of Technology in 1985. Since 1989, he has been at the University of Maryland, College Park, in the College of Business and Management. He was the Simulation Area Editor for Operations and is an Associate Editor for Management Science, and has served on the Editorial Boards ofthe INFORMS Journal on Computing, Production and Operations Management and IIE Transactions. He was on the program committee for the Spring 1996 INFORMS National Meeting, in charge of contributed papers. In 1995, he received the Maryland Business School's annual Allen J. Krowe Award for Teaching Excellence. He is the co-author (with Jian-Qiang Hu) of the book, Conditional Monte Carlo: Gradient Estimation and Optimization Applications (0-7923-9873-4, 1997), which received the 1998 INFORMS College on Simulation Outstanding Publication Award. Other awards include the 1999 IIE Operations Research Division Award and a 1998 IIE Transactions Best Paper Award. In 2002, he received ISR's Outstanding Systems Engineering Faculty Award. He currently serves as a director of National Science Foundation Operations Research Program. Dr. Fu's research interests lie in the areas of stochastic derivative estimation and simulation optimization of discrete-event systems, particularly with applications towards manufacturing systems, inventory control, and the pricing of financial derivatives. Steven I. Marcus (Fellow of the IEEE, Fellow of SIAM, Member of INFORMS) received his Ph.D. and S.M. from the Massachusetts Institute of Technology in 1975 and 1972, respectively. He received a B.A. from Rice University in 1971. From 1975 to 1991, he was with the Department of Electrical and Computer Engineering at the University of Texas at Austin, where he was the L.B. (Preach) Meaders Professor in Engineering. He was Associate Chairman of the Department during the period 1984-89. In 1991, he joined the University of Maryland, College Park, where he was Director of the Institute for Systems Research until 1996. He is currently a Professor in the Electrical Engineering Department and the Institute for Systems Research. He has served as an Editor of the SIAM Journal on Control and Optimization, and Associate Editor of Mathematics of Control, Signals, and Systems, Journal on Discrete Event Dynamic Systems, and Acta Applic

Inhalt
Markov Decision Processes.- Multi-stage Adaptive Sampling Algorithms.- Population-based Evolutionary Approaches.- Model Reference Adaptive Search.- On-line Control Methods via Simulation.